| 研究課題/領域番号 |
19F19081
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| 研究機関 | 東京大学 |
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研究代表者 |
山本 義春 東京大学, 大学院教育学研究科(教育学部), 教授 (60251427)
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| 研究分担者 |
QIAN KUN 東京大学, 教育学研究科(研究院), 外国人特別研究員
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| 研究期間 (年度) |
2019-10-11 – 2022-03-31
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| キーワード | Signal Processing / Internet of Things / Artificial Intelligence |
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| 研究実績の概要 |
In summary, we have achieved plenty of milestones during the FY2020. We introduced a novel paradigm that utilises the usage recorded data from smart appliances to analyse the elderly’s behaviour in a long duration. This non-intrusive approach can facilitate the combination of artificial intelligence and internet of things (AIoT) for making a more convenient and flexible life for the ageing population. This work was published online by the top journal IEEE Internet of Things Journal (with an impact factor of 9.936). Moreover, we systematically summarised the scenarios, data modalities, and methodologies for AIoT-enabled applications for the specific elderly group. We also indicated the benchmarks and limitations of the existing studies and gave our perspectives on future work. This article has been accepted and will be published by the prestigious journal IEEE Signal Processing Magazine (with an impact factor of 11.350). A comprehensive review was done and invited to be published by the IEEE Journal of Biomedical and Health Informatics (with an impact factor of 5.223). This review article concluded the state-of-the-art of audio-based methods for localising the snore site in the past three decades. In addition, we formed a team to collaboratively propose a novel approach for monitoring the confirmed COVID-19 patients on their sleep quality, fatigue, and anxiety. The relevant studies were published in the IEEE Internet of Things Journal and ISCA INTERSPEECH conference.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We are now working towards transferring our methods to more general purposes, e.g., monitoring the drowsiness of drivers via the spontaneous physical activity data. We are also investigating the advanced data augmentation methods for coping with the data scarcity challenge among the several applications, e.g., the audio-based COVID-19 diagnosis problem. Furthermore, we are exploring the optimal time-frequency methods for analysing the body sound signals. Some preliminary results have already been achieved in recent study on heart sound analysis work.
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| 今後の研究の推進方策 |
We will continuously collect more human behaviour data in near future, which may include multiple modalities, e.g., audio, video, and wearable sensors. We also want to build an explainable AI system for understanding the human behaviour in a high-level paradigm, which can benefit improving the model’s generalisation for multiple tasks.
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